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2016, Queueing Systems
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4 pages
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Professor emeritus Lajos Takács passed away on December 4, 2015, in Cleveland Heights, Ohio. He is survived by his wife Dalma, their two daughters Judith and Susan, and their families. Lajos Takács will be remembered as a brilliant mathematician, a groundbreaking contributor to the theory of stochastic processes, a world-leading queueing theorist, and a very kind person. We provide here a short biography and a discussion of his main contributions to queueing and fluctuation theory.
Springer eBooks, 1995
Finally, I would like to acknowledge my thesis advisor, Leonard Kleinrock, for the thrill of first seeing the beauty of applied probability through his eyes.
2008
We take a new look at transient, or time-dependent Little laws for queueing systems. Through the use of Palm measures, we will show that previous laws (see ) can be generalized; furthermore, within this framework a new law can be derived as well, which gives higher-moment expressions for very general types of queueing systems; in particular, the laws hold for systems that allow customers to overtake one another. What's especially novel about our approach is the use of Palm measures that are induced by nonstationary point processes, as these measures are not commonly found in the queueing literature. This new higher-moment law is then used to provide closed-form expressions of all moments of the number of customers in the system in an M/G/1 preemptive-LCFS queue at a time t > 0, for any initial condition and for any of the more famous preemptive disciplines (i.e. preemptive-resume, and preemptive-repeat with and without resampling). The phrase "closed-form" is used here to stress that the moments can be expressed in terms of probabilities that consist of convolutions of busy periods and residual busy periods, and so moment-matching methods can be used to generate very simple approximations of these quantities, as in . It is also worth noting that these results appear to be new for the M/M/1 queue as well (see , [4]), and so we use them to derive a nice structural form for all of the time-dependent moments of a regulated Brownian motion (see [1], [2]).
HAL (Le Centre pour la Communication Scientifique Directe), 2016
Kleinrock (1964) proposed a queueing discipline for a single-server queue in which customers from different classes accumulate priority as linear functions of their waiting time. When the server becomes free, it selects the waiting customer with the highest amount of accumulated priority at that instant, provided that the queue is nonempty. For such a queue, Kleinrock developed a recursion for calculating the expected waiting time of customers from each class. More recently, Stanford, Taylor and Ziedins (2014) took another look at this queue, which they termed the Accumulating Priority Queue (APQ), and derived the waiting time distributions for each class. Kleinrock and Finkelstein (1967) also studied an accumulating priority system in which customers' priorities increase as a power-law function of their time in the queue. They established that it is possible to associate a particular linear accumulating priority queue with such a power-law accumulating priority queue, in such a way that the expected waiting times of customers from the different classes are preserved. In this paper, we extend their analysis to characterise the class of nonlinear accumulating priority queues for which an equivalent linear APQ can be found, in the sense that the waiting time distributions for each of the classes are identical in both the linear and nonlinear systems.
The Annals of Applied Probability, 1991
We consider a series of n single-server queues, each with unlimited waiting space and the first-in first-out service discipline. Initially, the system is empty; then k customers are placed in the first queue. The service times of all the customers at all the queues are i.i.d. with a general distribution. We are interested in the time D(k, n) required for all k customers to complete service from all n queues. In particular, we investigate the limiting behavior of D(k, n) as n → ∞ and/or k → ∞. There is a duality implying that D(k, n) is distributed the same as D(n, k) so that results for large n are equivalent to results for large k. A previous heavy-traffic limit theorem implies that D(k, n) satisfies an invariance principle as n → ∞, converging after normalization to a functional of k-dimensional Brownian motion. We use the subadditive ergodic theorem and a strong approximation to describe the limiting behavior of D(k n , n) where k n → ∞ as n → ∞. The case of k n = xn corresponds to a hydrodynamic limit.
csm.ro
We study a class of Lindley processes whose distribution can be computed. We call them computable Lindley processes and investigate their applications in queueing theory and ruin theory. AMS 2000 Subject Classification: 60J15, 60K10, 60K25, 60G99. ... Key words: queueing ...
2002
We present a possible way to extend queuing theory to account for interactions between adjacent queues in a packet-switched network. The interaction between queues arises because of the influence of the routing protocol on each switching decision and the stochastic nature of packet lengths and inter-arrival times. Both the methodology and the analysis tools are adaptations of methods of statistical mechanics and are presented in outline here. The justification for their use lies in experimental evidence given in [1,2,3] that aggregate, core-network IP traffic exhibits quasi-Markovian properties. In this paper, we focus on the interaction between pairs of queues, either in a cascaded arrangement, or connected to the same switching fabric, in the presence of an idealised routing protocol.
2012
Queueing theory is concerned with developing and investigating mathematical models of systems where customers wait for service. The terms customers and servers are generic. Customers could, for example, be humans waiting in a physical line or waiting on hold on the telephone, jobs waiting to be processed in a factory, or tasks waiting for processing in a computer or communication system. Examples of service include a medical procedure, a phone call, or a commercial transaction. Queueing theory started with the work of Danish mathematician A. K. Erlang in 1905, which was motivated by the problem of designing telephone exchanges. The field has grown to include the application of a variety of mathematical methods to the study of waiting lines in many different contexts. The mathematical methods include Markov processes, linear algebra, transform theory, and asymptotic methods, to name a few. The areas of application include computer and communication systems, manufacturing systems, and...
1997
In this paper we present a combinatorial technique which allows the derivation of the transition functions of general birth-death processes. This method provides a flexible tool for the transient analysis of Markovian queueing systems with state dependent transition rates, like M/M/c models or systems with balking and reneging.
Queueing Systems, 2008
We analyze the output process of finite capacity birth-death Markovian queues. We develop a formula for the asymptotic variance rate of the form λ * + v i where λ * is the rate of outputs and vi are functions of the birth and death rates. We show that if the birth rates are non-increasing and the death rates are non-decreasing (as is common in many queueing systems) then the values of v i are strictly negative and thus the limiting index of dispersion of counts of the output process is less than unity. In the M/M/1/K case, our formula evaluates to a closed form expression that shows the following phenomenon: When the system is balanced, i.e. the arrival and service rates are equal, v i λ * is minimal.
2016
Queueing Theory is one of the most commonly used mathematical tool for the performance evaluation of systems. The aim of the book is to present the basic methods, approaches in a Markovian level for the analysis of not too complicated systems. The main purpose is to understand how models could be constructed and how to analyze them. It is intended not only for students of computer science, engineering, operation research, mathematics but also those who study at business, management and planning departments, too. It covers more than one semester and has been tested by graduate students at Debrecen University over the years. It gives a very detailed analysis of the involved queueing systems by giving density function, distribution function, generating function, Laplace-transform, respectively. Furthermore, Java-applets are provided to calculate the main performance measures immediately by using the pdf version of the book in a WWW environment. I have attempted to provide examples for ...
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